Students will acquire the basics for understanding and developing computerised decision support tools:
(1) Overview of methods and tools of simulation
(2) Overview of methods and tools of approximate optimisation
Heuristics (Greedy algorithms, descent direction algorithms, …)
Meta-heuristics based on local research (simulated annealing, Tabu research)
Evolutionary meta-heuristics (genetic algorithms, Ant colonies …)
(3) Overview of methods and tools of exact optimisation (Unit 1 is a pre-requisite for this section)
Methods by separation and evaluation
Cutting methods
(4) Multi-goal optimisation
(5) Links and combinations between these different methods
| On completion of the unit, the student will be capable of: | Classification level | Priority |
|---|---|---|
| Understanding the principle computerised decision support tools | 2. Understand | Essential |
| Percentage ratio of individual assessment | Percentage ratio of group assessment | ||||
|---|---|---|---|---|---|
| Written exam: | % | Project submission: | % | ||
| Individual oral exam: | % | Group presentation: | % | ||
| Individual presentation: | % | Group practical exercise: | % | ||
| Individual practical exercise: | 100 | % | Group report: | % | |
| Individual report: | % | ||||
| Other(s): % | |||||
| Type of teaching activity | Content, sequencing and organisation |
|---|---|
| Course | Lecture courses (12 h) |
| Supervised studies | Modelling exercises (4.5 h) |
| Practical courses | Problem solving (10.5 h) |
| Conference |